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   "source": [
    "# Python Data Science Handbook\n",
    "\n",
    "# Python数据科学手册"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Jake VanderPlas*\n",
    "\n",
    "原作者 *Jake VanderPlas*\n",
    "\n",
    "译者 *[wangyingsm@github.com](https://github.com/wangyingsm)*\n",
    "\n",
    "![Book Cover](https://github.com/wangyingsm/Python-Data-Science-Handbook/raw/master/notebooks/figures/PDSH-cover.png)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "> This is the Jupyter notebook version of the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!\n",
    "\n",
    "这是Jake VenderPlas所著的[Python数据科学手册](http://shop.oreilly.com/product/0636920034919.do)的Jupyter notebook版本；本内容在[GitHub](https://github.com/wangyingsm/Python-Data-Science-Handbook)上。文字发行协议遵循[CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode)协议，而代码发行遵循[MIT license](https://opensource.org/licenses/MIT)。如果你认为这些内容很有用，请考虑通过[购买本书](http://shop.oreilly.com/product/0636920034919.do)支持作者。"
   ]
  },
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   "source": [
    "## 目录\n",
    "\n",
    "译者注：原版英文目录见下方\n",
    "\n",
    "#### [序言](00.00-Preface.ipynb)\n",
    "\n",
    "#### [1. IPython：超越Python解释器](01.00-IPython-Beyond-Normal-Python.ipynb)\n",
    "- [IPython帮助和文档](01.01-Help-And-Documentation.ipynb)\n",
    "- [IPython Shell中的键盘快捷键](01.02-Shell-Keyboard-Shortcuts.ipynb)\n",
    "- [IPython魔术命令](01.03-Magic-Commands.ipynb)\n",
    "- [输入输出历史](01.04-Input-Output-History.ipynb)\n",
    "- [IPython和Shell命令](01.05-IPython-And-Shell-Commands.ipynb)\n",
    "- [错误和调试](01.06-Errors-and-Debugging.ipynb)\n",
    "- [性能测算和计时](01.07-Timing-and-Profiling.ipynb)\n",
    "- [更多IPython资源](01.08-More-IPython-Resources.ipynb)\n",
    "\n",
    "#### [2. Numpy介绍](02.00-Introduction-to-NumPy.ipynb)\n",
    "- [理解Python中的数据类型](02.01-Understanding-Data-Types.ipynb)\n",
    "- [Numpy数组基础](02.02-The-Basics-Of-NumPy-Arrays.ipynb)\n",
    "- [使用Numpy计算：通用函数](02.03-Computation-on-arrays-ufuncs.ipynb)\n",
    "- [聚合：Min, Max, 以及其他](02.04-Computation-on-arrays-aggregates.ipynb)\n",
    "- [在数组上计算：广播](02.05-Computation-on-arrays-broadcasting.ipynb)\n",
    "- [比较，遮盖和布尔逻辑](02.06-Boolean-Arrays-and-Masks.ipynb)\n",
    "- [高级索引](02.07-Fancy-Indexing.ipynb)\n",
    "- [数组排序](02.08-Sorting.ipynb)\n",
    "- [格式化数据：NumPy里的结构化数组](02.09-Structured-Data-NumPy.ipynb)\n",
    "\n",
    "#### [3. 使用Pandas进行数据处理](03.00-Introduction-to-Pandas.ipynb)\n",
    "- [Pandas对象简介](03.01-Introducing-Pandas-Objects.ipynb)\n",
    "- [数据索引和选择](03.02-Data-Indexing-and-Selection.ipynb)\n",
    "- [在Pandas中操作数据](03.03-Operations-in-Pandas.ipynb)\n",
    "- [处理空缺数据](03.04-Missing-Values.ipynb)\n",
    "- [层次化的索引](03.05-Hierarchical-Indexing.ipynb)\n",
    "- [组合数据集：Concat 和 Append](03.06-Concat-And-Append.ipynb)\n",
    "- [组合数据集：Merge 和 Join](03.07-Merge-and-Join.ipynb)\n",
    "- [聚合与分组](03.08-Aggregation-and-Grouping.ipynb)\n",
    "- [数据透视表](03.09-Pivot-Tables.ipynb)\n",
    "- [向量化的字符串操作](03.10-Working-With-Strings.ipynb)\n",
    "- [在时间序列上操作](03.11-Working-with-Time-Series.ipynb)\n",
    "- [高性能Pandas: ``eval()`` 和 ``query()``](03.12-Performance-Eval-and-Query.ipynb)\n",
    "- [更多资源](03.13-Further-Resources.ipynb)\n",
    "\n",
    "#### [4. 使用matplotlib展示数据](04.00-Introduction-To-Matplotlib.ipynb)\n",
    "- [简单的折线图](04.01-Simple-Line-Plots.ipynb)\n",
    "- [简单的散点图](04.02-Simple-Scatter-Plots.ipynb)\n",
    "- [误差可视化](04.03-Errorbars.ipynb)\n",
    "- [密度和轮廓图](04.04-Density-and-Contour-Plots.ipynb)\n",
    "- [直方图, 分桶和密度](04.05-Histograms-and-Binnings.ipynb)\n",
    "- [自定义图表图例](04.06-Customizing-Legends.ipynb)\n",
    "- [自定义颜色条](04.07-Customizing-Colorbars.ipynb)\n",
    "- [多个子图表](04.08-Multiple-Subplots.ipynb)\n",
    "- [文本和标注](04.09-Text-and-Annotation.ipynb)\n",
    "- [自定义刻度](04.10-Customizing-Ticks.ipynb)\n",
    "- [自定义matplotlib：配置和样式单](04.11-Settings-and-Stylesheets.ipynb)\n",
    "- [在matplotlib中创建三维图表](04.12-Three-Dimensional-Plotting.ipynb)\n",
    "- [使用Basemap创建地理位置图表](04.13-Geographic-Data-With-Basemap.ipynb)\n",
    "- [使用Seaborn进行可视化](04.14-Visualization-With-Seaborn.ipynb)\n",
    "- [更多资源](04.15-Further-Resources.ipynb)\n",
    "\n",
    "#### [5. 机器学习](05.00-Machine-Learning.ipynb)\n",
    "- [什么是机器学习？](05.01-What-Is-Machine-Learning.ipynb)\n",
    "- [Scikit-Learn简介](05.02-Introducing-Scikit-Learn.ipynb)\n",
    "- [超参数及模型验证](05.03-Hyperparameters-and-Model-Validation.ipynb)\n",
    "- [特征工程](05.04-Feature-Engineering.ipynb)\n",
    "- [深入：朴素贝叶斯分类](05.05-Naive-Bayes.ipynb)\n",
    "- [深入：线性回归](05.06-Linear-Regression.ipynb)\n",
    "- [深入：支持向量机](05.07-Support-Vector-Machines.ipynb)\n",
    "- [深入：决策树和随机森林](05.08-Random-Forests.ipynb)\n",
    "- [深入：主成分分析](05.09-Principal-Component-Analysis.ipynb)\n",
    "- [深入：流形学习](05.10-Manifold-Learning.ipynb)\n",
    "- [深入：k-均值聚类](05.11-K-Means.ipynb)\n",
    "- [深入：高斯混合模型](05.12-Gaussian-Mixtures.ipynb)\n",
    "- [深入：核密度估计](05.13-Kernel-Density-Estimation.ipynb)\n",
    "- [应用：脸部识别管道](05.14-Image-Features.ipynb)\n",
    "- [更多机器学习资源](05.15-Learning-More.ipynb)\n",
    "\n",
    "#### [附录：生成图像的代码](06.00-Figure-Code.ipynb)"
   ]
  },
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   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "#### [Preface](00.00-Preface.ipynb)\n",
    "\n",
    "#### [1. IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)\n",
    "- [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)\n",
    "- [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb)\n",
    "- [IPython Magic Commands](01.03-Magic-Commands.ipynb)\n",
    "- [Input and Output History](01.04-Input-Output-History.ipynb)\n",
    "- [IPython and Shell Commands](01.05-IPython-And-Shell-Commands.ipynb)\n",
    "- [Errors and Debugging](01.06-Errors-and-Debugging.ipynb)\n",
    "- [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)\n",
    "- [More IPython Resources](01.08-More-IPython-Resources.ipynb)\n",
    "\n",
    "#### [2. Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb)\n",
    "- [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb)\n",
    "- [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb)\n",
    "- [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)\n",
    "- [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)\n",
    "- [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)\n",
    "- [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)\n",
    "- [Fancy Indexing](02.07-Fancy-Indexing.ipynb)\n",
    "- [Sorting Arrays](02.08-Sorting.ipynb)\n",
    "- [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb)\n",
    "\n",
    "#### [3. Data Manipulation with Pandas](03.00-Introduction-to-Pandas.ipynb)\n",
    "- [Introducing Pandas Objects](03.01-Introducing-Pandas-Objects.ipynb)\n",
    "- [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)\n",
    "- [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb)\n",
    "- [Handling Missing Data](03.04-Missing-Values.ipynb)\n",
    "- [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb)\n",
    "- [Combining Datasets: Concat and Append](03.06-Concat-And-Append.ipynb)\n",
    "- [Combining Datasets: Merge and Join](03.07-Merge-and-Join.ipynb)\n",
    "- [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)\n",
    "- [Pivot Tables](03.09-Pivot-Tables.ipynb)\n",
    "- [Vectorized String Operations](03.10-Working-With-Strings.ipynb)\n",
    "- [Working with Time Series](03.11-Working-with-Time-Series.ipynb)\n",
    "- [High-Performance Pandas: eval() and query()](03.12-Performance-Eval-and-Query.ipynb)\n",
    "- [Further Resources](03.13-Further-Resources.ipynb)\n",
    "\n",
    "#### [4. Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb)\n",
    "- [Simple Line Plots](04.01-Simple-Line-Plots.ipynb)\n",
    "- [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb)\n",
    "- [Visualizing Errors](04.03-Errorbars.ipynb)\n",
    "- [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)\n",
    "- [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb)\n",
    "- [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)\n",
    "- [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb)\n",
    "- [Multiple Subplots](04.08-Multiple-Subplots.ipynb)\n",
    "- [Text and Annotation](04.09-Text-and-Annotation.ipynb)\n",
    "- [Customizing Ticks](04.10-Customizing-Ticks.ipynb)\n",
    "- [Customizing Matplotlib: Configurations and Stylesheets](04.11-Settings-and-Stylesheets.ipynb)\n",
    "- [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb)\n",
    "- [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)\n",
    "- [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb)\n",
    "- [Further Resources](04.15-Further-Resources.ipynb)\n",
    "\n",
    "#### [5. Machine Learning](05.00-Machine-Learning.ipynb)\n",
    "- [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb)\n",
    "- [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)\n",
    "- [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)\n",
    "- [Feature Engineering](05.04-Feature-Engineering.ipynb)\n",
    "- [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)\n",
    "- [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)\n",
    "- [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)\n",
    "- [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)\n",
    "- [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)\n",
    "- [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)\n",
    "- [In Depth: k-Means Clustering](05.11-K-Means.ipynb)\n",
    "- [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)\n",
    "- [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb)\n",
    "- [Application: A Face Detection Pipeline](05.14-Image-Features.ipynb)\n",
    "- [Further Machine Learning Resources](05.15-Learning-More.ipynb)\n",
    "\n",
    "#### [Appendix: Figure Code](06.00-Figure-Code.ipynb)"
   ]
  }
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